Beyond Genes and Environment, Random Variations Play Important Role in Longevity

A new model of aging takes into account not only genetics and environmental exposures but also the tiny changes that randomly arise at the cellular level.

University Professor Caleb Finch introduced the “Tripartite Phenotype of Aging” as a new conceptual model that addresses why lifespan varies so much, even among human identical twins who share the same genes. Only about 10 to 35 percent of longevity can be traced to genes inherited from our parents, Finch mentioned.

Finch authored the paper introducing the model with one of his former graduate students, Amin Haghani, who received his PhD in the Biology of Aging from the USC Leonard Davis School in 2020 and is now a postdoctoral researcher at UCLA. In the article, they propose that the limited heritability of aging patterns and longevity in humans is an outcome of gene-environment interactions, together with stochastic, or chance, variations in the body’s cells. These random changes can include cellular changes that happen during development, molecular damage that occurs later in life, and more.

“We wanted to introduce a conceptual map and some new terminology that will motivate a more comprehensive understanding of what the limitations of genetic determinants in aging are, how important it is to consider the genetic variance in relationship to the environment, and include this new domain of stochastic variations, which is very well recognized by different fields,” said Finch, who holds the ARCO/William F. Kieschnick Chair in the Neurobiology of Aging at the USC Leonard Davis School. “It hasn’t really been put in a formal context in which the complete package can be discussed, and that’s what I hope our article achieves.”

Expanding on the exposome

The new model is a natural extension of the idea of the exposome, which was first proposed by cancer epidemiologist Christopher Paul Wild in 2005 to draw attention to the need for more data on lifetime exposure to environmental carcinogens. The exposome concept illustrates how external factors, ranging from air pollution and socioeconomic status to individual diet and exercise patterns, interact with endogenous, or internal, factors such as the body’s microbiome and fat deposits.

The exposome is now a mainstream model, eclipsing previous characterizations of environmental factors as affecting risk “one by one.” Finch has previously expanded on the exposome concept with the introduction of the Alzheimer’s disease exposome. The gero-exposome now considers how genes and the environment interact over the lifespan to shape how we age.

The new model illustrates that cell-by-cell variations in gene expression, variations arising during development, random mutations, and epigenetic changes – turning genes “off” or “on” – should be explicitly considered apart from traditional genetic or environmental research regarding aging, Finch said. More detailed study into these chance processes has been enabled by cutting-edge research techniques, including the study of gene transcription within single cells as well as ChIP-sequencing, which can illustrate how individual proteins interact with DNA.

Effects of happenstance on health

In the paper, Finch and Haghani discussed several examples of how risks of age-related disease are poorly predicted by DNA alone but are heavily influenced by environmental exposures as well as the time and duration of the exposure, including during development or over the course of decades.

One well-known example of a gene that is associated with increased Alzheimer’s risk is ApoE-4; however, having the ApoE-4 gene doesn’t definitively mean someone will get Alzheimer’s. Studies in both mice and humans revealed that ApoE-4 and clusters of related genes interact with exposures such as air pollution or cigarette smoke to influence risk, and Alzheimer’s patients also show differences in their epigenetics as compared to individuals without the disease.

He added that the idea of environmental exposure can stretch farther than many people expect. Disease exposure earlier in life can affect health risks later in life – and across generations.

“The environment that we’re exposed to goes back to our grandmothers because the egg we came from was in our mother’s ovaries at the time of her birth,” he explained. “So that means, in my case, because my grandmother was born in 1878, I might very well carry some traces of the 19th century environment, which included much greater exposure to infectious disease because there were no antibiotics.”

Finch said that he hopes the more comprehensive model on how genes, environment, and random variations over time interact to influence aging prompt a new discussion of what the rapidly developing field of precision medicine needs to take into account to promote healthy aging.

“I think that there will be a much greater recognition in understanding individual patterns of aging,” he said. “We can only define it up to a certain point by knowing the genetic risks; we must have a more comprehensive understanding of the lifetime exposures, environments and lifestyles of an individual to have a better understanding of genetic risk for particular diseases.”

Source: EurekAlert!

Genomic Test Helps Estimate Risk of Prostate Cancer Metastasis, Death

A commercially available genomic test may help oncologists better determine which patients with recurrent prostate cancer may benefit from hormone therapy, according to new research from the Johns Hopkins Kimmel Cancer Center and 15 other medical centers.

Researchers studied prostate cancer samples from 352 participants in the NRG/RTOG 9601 clinical trial, which compared radiation therapy alone with radiation therapy combined with hormone therapy. The investigators found that the Decipher test, which measures the activity of 22 genes among seven known cancer pathways, independently estimated the participants’ risk of metastasis, death from prostate cancer and overall survival. Researchers say it also guided treatment recommendations for recurrence of prostate cancer after surgery, helping identify patients most likely to benefit from hormone therapy.

Results were published online in the journal JAMA Oncology.

“The findings may be practice-changing, and will give oncologists additional information to help guide decisions on whether to offer patients hormone therapy,” says senior study author Phuoc Tran, M.D., Ph.D., professor of radiation oncology and molecular radiation sciences and co-director of the Cancer Invasion and Metastasis Program at the Johns Hopkins Kimmel Cancer Center.

For the study, Tran and colleagues studied prostate cancer samples from 352 men whose prostate cancer recurred after surgery and who participated in the NRG/RTOG 9601 clinical trial of radiation therapy between March 1998 and March 2003. The participants were randomized to receive radiation with hormone therapy (150 milligrams of bicalutamide daily for two years) or radiation therapy without hormone therapy. The researchers ran genetic information called ribonucleic acid (RNA) from the tumor tissue through the Decipher test, which evaluates the activity of 22 genes to predict how aggressive the cancer is and its chances of metastasis.

The test uses “genomic classifier scores” determined from the genomic characteristics of the tumor to stratify patients into three groups. Lower scores correlate with a more favorable prognosis. Of the participants, 148 patients (42%) had low genomic classifier scores, below .45; 132 (38%) had intermediate genomic classifier scores, between .45 and .60; and 72 (20%) had high genomic classifier scores, above .60. These genomic classifiers helped predict the risk of distant metastases, prostate cancer-specific death and overall survival, even after adjusting for participants’ age, race/ethnicity, Gleason score, T stage of T1 to T4 to classify the extent of tumor spread, margin status, prostate-specific antigen level and whether they were receiving hormone therapy.

Specifically, patients with intermediate and high genomic classifier scores had an 88% increased risk of distant metastases versus those with low genomic classifier scores. The test also demonstrated that patients with lower genomic classifier scores had a 2.4% improvement in overall survival 12 years after treatment with radiation and hormone therapy, compared with an 8.9% improvement among those with higher genomic classifier scores. Additionally, patients receiving radiation therapy soon after recurrence benefited more from hormone therapy. Those with higher genomic classifier scores derived an 11.2% improvement in 12-year occurrence of distant metastasis and a 4.6% improvement in overall survival from taking hormone therapy.

“Patients with lower scores do better overall, but they do not benefit as much from adding on hormone therapy because they are low risk. Patients with higher scores have worse disease and, therefore, benefit the most from adding the hormone therapy,” explains Tran.

“The way that we treat many patients in the clinic now is based on pathological and clinical factors, such as age, stage, grade and imaging,” Tran says. “Those have been very helpful, but you can only go so far with these rough markers. Hormone therapy unfortunately has a whole host of side effects, such as lack of libido, lack of erections and fatigue, and over time can increase the risk of diabetes, heart attack or stroke, so you only want to prescribe it if it is clearly beneficial. Our study demonstrated that Decipher is prognostic in its ability to determine cancer metastasis and also prostate cancer-specific survival and overall survival. It also was able to determine the benefit of patients receiving hormones or no hormones.” Tran says the information the test provides guides precision medicine efforts to help physicians direct hormone therapies to those most likely to benefit from them.

Source: The Johns Hopkins University

Daytime Napping May Be in Your Genes

If you like to take a snooze in the afternoon, your genes may explain your love of daytime naps, researchers say.

For their study, investigators analyzed data from the UK Biobank, which contains genetic information from nearly 453,000 people who were asked how often they nap during the day.

The genome-wide association study identified 123 regions in the human genome that are associated with daytime napping. Many genes near or at those regions are known to play a role in sleep.

A subset of participants wore activity monitors that provided data about daytime inactivity, which can be an indicator of napping. That data suggested that the participants’ self-reported information about napping was accurate, according to the researchers.

“That gave an extra layer of confidence that what we found is real and not an artifact,” said study co-lead author Hassan Saeed Dashti, from the Center for Genomic Medicine at Massachusetts General Hospital in Boston.

The researchers also replicated their findings in an analysis of genetic data from more than 541,000 people collected by the consumer genetic-testing company 23andMe.

The findings show “that daytime napping is biologically driven, and not just an environmental or behavioral choice,” Dashti said in a hospital news release.

Several gene variants linked to napping are associated with signaling by a neuropeptide called orexin, which plays a role in wakefulness, according to study co-lead author Iyas Daghlas, a medical student at Harvard Medical School.

“This pathway is known to be involved in rare sleep disorders like narcolepsy, but our findings show that smaller perturbations in the pathway can explain why some people nap more than others,” Daghlas said.

Some of those gene variants also have a connection with heart health risk factors, such as a large waist circumference and elevated blood pressure. But more research on those links is needed, according to the report published Feb. 10 in the journal Nature Communications.

The researchers also identified at least three possible factors associated with daytime napping: Some people need more sleep than others; people who wake up early may need to catch up on their sleep with a nap; and daytime naps can make up for poor quality sleep the previous night.

According to co-senior author Marta Garaulet, from the department of physiology at the University of Murcia, in Spain, “Future work may help to develop personalized recommendations for siesta.”

Source: HealthDay

The Impact of BMI and Genes on the Risk of Developing Diabetes

Brian Ference wrote . . . . . . . . .

This study included 445,765 participants from the UK Biobank, of whom 31,298 developed diabetes after age 25 years. A PGS composed of 2,137,820 variants for diabetes was constructed and this was used to divide the population into quintiles to quantify the effect of increasing polygenic predisposition on risk of diabetes. A step­wise increase in the risk of diabetes with increasing PGS was observed such that participants in the highest as compared with the lowest PGS quintile had a hazard ratio (HR) for diabetes of 2.99 (95% confidence interval [CI] 2.90–3.14; p<0.001). There was also a step­wise increase in the risk of diabetes with increasing BMI. Participants in the highest BMI quintile (mean BMI 35 kg/m2) as compared with the lowest (mean BMI 22 kg/m2) had a HR for diabetes of 11.42 (95% CI 10.81–12.07; p<0.001).

The investigators then evaluated how much the risk of diabetes varied at each level of the PGS depending on differences in BMI. The effects of PGS and BMI on the risk of diabetes were independent and additive. Within each quintile of PGS, the risk of diabetes varied by more than 10­fold depending on differences in BMI. As a result, participants in the lowest PGS quintile with high BMI had a much greater risk of diabetes than participants in the highest PGS quintile with low BMI.

“These findings indicate that BMI is a much more powerful risk factor for diabetes than polygenic predisposition, but a PGS can moderately improve estimates of lifetime risk of diabetes at all levels of BMI,” said Prof. Ference.

Next, the investigators sought to determine if elevated BMI has a cumulative or threshold effect on the risk of developing diabetes. To conduct this analysis, the investigators constructed a genetic score for BMI composed of 255 variants independently associated with BMI at the genome-wide level of significance to assess the effect of lifetime exposure to increased BMI on the risk of diabetes. They found that the effect of a one unit increase in lifetime exposure to BMI on the risk of diabetes in Mendelian randomisation analyses was approximately the same as a one unit increase in BMI measured in middle life in observational analyses, suggesting that lifetime exposure to increased BMI does not have a cumulative effect on the risk of developing diabetes.

Prof. Ference explained, “BMI appears to have a threshold effect rather than a cumulative effect on the risk of diabetes – the BMI level at which a person develops insulin resistance and hyperglycaemia is their diabetes threshold.” He continued, “The findings indicate that most cases of diabetes could be avoided, or reversed, by keeping BMI below the cut-off which triggers insulin resistance in each individual.”

“Both BMI and blood glucose level should be assessed regularly to prevent diabetes,” Prof. Ference concluded. “Furthermore, efforts to lose weight are critical when a person starts to develop hyperglycaemia as it may be possible to reverse diabetes by losing weight before permanent damage occurs.”


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Your Genes May Affect How You’ll Heal If Wounded

Your genes may have a big impact on bacteria in your wounds and how quickly you heal, new research shows.

The researchers said their findings could help improve wound treatment.

Chronic wounds — ones that don’t show signs of healing within three weeks — can be costly, and bacterial infection slows the process.

A range of bacterial species are present in chronic wounds, but it’s not clear why certain ones are found in some wound infections and not others.

In order to learn more, the researchers investigated the link between genes and bacteria diversity in chronic wounds.

They linked variations in two key genes — TLN2 and ZNF521 — to both the number of bacteria in wounds and the abundance of harmful ones, primarily Pseudomonas aeruginosa and Staphylococcus epidermidis.

Pseudomonas-infected wounds had fewer species of bacteria — and wounds with fewer species were slower to heal, the investigators found.

The results suggest that genetic variation influences the types of bacteria that infect wounds as well as the healing process.

The study by Caleb Phillips, an assistant professor of biology at Texas Tech University in Lubbock, and colleagues was published online June 18 in the journal PLOS Pathogens.

The authors described their study as the first to identify how genes influence wound bacteria and healing.

“This study demonstrates the ability to find variants in people’s genomes that explain differences in the microorganisms that infect their wounds. Such information is expected to guide new understanding about the mechanisms of infection and healing, and the establishment of predictive biomarkers that improve patient care,” the authors said in a journal news release.

Source: HealthDay


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